Analyzing user searches of verbal media content

ABSTRACT

Disclosed are various embodiments for analyzing user searches of verbal media content associated with media content features. A search query is obtained from a user. Media content items are determined by executing a verbal media content search based at least in part on the search query. The media content items include verbal media content that is relevant to the search query. Data relating to the verbal media content search is stored. A user interest in media content is determined by analyzing the data relating to the verbal media content search.

BACKGROUND

Certain portions of a movie or other media content may be moreinteresting to users than other portions. For example, a movie may havea particularly memorable scene with sophisticated visual effects. Movieproducers may select such a scene for inclusion in a trailer. Likewise,an episode of a television series may have a particular scene withpowerful dialogue. The series producer may choose to highlight a portionof the scene within advertising promoting the airing of the episode.

BRIEF DESCRIPTION OF THE DRAWINGS

Many aspects of the present disclosure can be better understood withreference to the following drawings. The components in the drawings arenot necessarily to scale, with emphasis instead being placed uponclearly illustrating the principles of the disclosure. Moreover, in thedrawings, like reference numerals designate corresponding partsthroughout the several views.

FIG. 1 is a drawing of a networked environment according to variousembodiments of the present disclosure.

FIG. 2 is a drawing of an example of a user interface rendered by aclient in the networked environment of FIG. 1 according to variousembodiments of the present disclosure.

FIG. 3 is a flowchart illustrating one example of functionalityimplemented as portions of a verbal media content search engine executedin a computing environment in the networked environment of FIG. 1according to various embodiments of the present disclosure.

FIG. 4 is a schematic block diagram that provides one exampleillustration of a computing environment employed in the networkedenvironment of FIG. 1 according to various embodiments of the presentdisclosure.

DETAILED DESCRIPTION

The present disclosure relates to determining user interest in mediacontent expressed through searches of verbal media content. Mediacontent, such as video content and/or audio content, may include verbalmedia content. Non-limiting examples of such media content may includemovies, television shows, audio books, songs, and so on. Non-limitingexamples of such verbal media content may include movie dialogue,television dialogue, audio book clips, song lyrics, and others.

Search functionality may be provided to search within verbal mediacontent. Corresponding portions of the media content may be surfaced tousers as search results in response to verbal media content searches. Toillustrate, a user may search for a particular quote within a moviedialogue database. Various matches may be found and ranked according torelevancy and/or other factors. Along with each match, a clip of a moviemay be surfaced to the user, where the clip features dialogue thatmatches the search query. The clip may be the particular scene featuringthe dialogue. If the user is interested in the clip, the user may selectthe clip for viewing, download, purchase, adding to a watch list, etc.

Various embodiments of the present disclosure gather user interest datapertaining to verbal media content searches and selections of searchresults. The user interest data may be analyzed to drive recommendationsof media content and/or portions of media content. Customizedcompilations of media content may be generated based at least in part onportions of media content selected by users as part of a verbal mediacontent search. Similarities and relationships among different users maybe determined from the user interest data. In the following discussion,a general description of the system and its components is provided,followed by a discussion of the operation of the same.

With reference to FIG. 1, shown is a networked environment 100 accordingto various embodiments. The networked environment 100 includes acomputing environment 103 and one or more clients 106 in datacommunication via a network 109. The network 109 includes, for example,the Internet, intranets, extranets, wide area networks (WANs), localarea networks (LANs), wired networks, wireless networks, or othersuitable networks, etc., or any combination of two or more suchnetworks.

The computing environment 103 may comprise, for example, a servercomputer or any other system providing computing capability.Alternatively, the computing environment 103 may employ a plurality ofcomputing devices that may be employed that are arranged, for example,in one or more server banks or computer banks or other arrangements.Such computing devices may be located in a single installation or may bedistributed among many different geographical locations. For example,the computing environment 103 may include a plurality of computingdevices that together may comprise a cloud computing resource, a gridcomputing resource, a content delivery network, and/or any otherdistributed computing arrangement. In some cases, the computingenvironment 103 may correspond to an elastic computing resource wherethe allotted capacity of processing, network, storage, or othercomputing-related resources may vary over time.

Various applications and/or other functionality may be executed in thecomputing environment 103 according to various embodiments. Also,various data is stored in a data store 112 that is accessible to thecomputing environment 103. The data store 112 may be representative of aplurality of data stores 112 as can be appreciated. The data stored inthe data store 112, for example, is associated with the operation of thevarious applications and/or functional entities described below.

The components executed on the computing environment 103, for example,include a network content server 115, a verbal media content searchengine 118, a search analysis application 121, an abridgement generationapplication 124, a recommendations application 127, and otherapplications, services, processes, systems, engines, or functionalitynot discussed in detail herein. The network content server 115 isexecuted to serve up network content such as, for example, web pages,mobile application data, and/or other forms of network content. Thenetwork content server 115 may comprise a commercially availablehypertext transfer protocol (HTTP) server such as, for example, Apache®HTTP Server, Apache® Tomcat®, Microsoft® Internet Information Services(IIS), and others. It is noted that multiple different network contentservers 115 may be employed in the computing environment 103 in variousembodiments. For purposes of discussion, the network content server 115is referred to herein in the singular.

The verbal media content search engine 118 is executed to performsearches of verbal media content corresponding to media content. To thisend, the verbal media content search engine 118 obtains a search query130 from a user at the client 106 and then generates search results 133based at least in part on the search query 130. The verbal media contentsearch engine 118 may be configured to record data relating to suchsearches, such as the search queries 130, the search results 133, searchresult selections 136, and/or other data.

The search analysis application 121 is executed to analyze this datarelating to the verbal media content searches for various purposes. Forexample, in one embodiment, the analysis performed by the searchanalysis application 121 may be used by the abridgement generationapplication 124 to generate a customized abridgement of a media contentfeature for a user or group of users. In another embodiment, theanalysis performed by the search analysis application 121 may be used bythe recommendations application 127 to generate recommendations 139 ofadditional media content to a user or to generate recommendations 139 ofuser-selected portions of media content to other users. In anotherembodiment, the analysis performed by the search analysis application121 may be used to select a portion of a media content feature as arepresentative portion for previewing. In yet another embodiment, theanalysis performed by the search analysis application 121 may be used tomarket products to a user.

The data stored in the data store 112 includes, for example, mediacontent features 142, verbal media content data 145, verbal time codedata 148, search data 151, user interest data 154, user profile data157, recommendations data 160, abridgement data 163, social network data166, representative content portions data 169, and potentially otherdata. The media content features 142 correspond to features of mediacontent in various forms that may be made available to clients 106. Forexample, the media content features 142 may correspond to video contentfeatures such as movies, television shows, video clips, and so on. Themedia content features 142 may also correspond to audio content featuressuch as songs, audio books, and others. It is noted that video contentfeatures may include corresponding audio content. The media contentfeatures 142 may be divided into scenes, segments, chapters, pages,and/or other parts or portions.

The verbal media content data 145 includes data relating to the verbalmedia content of the media content features 142. Such verbal mediacontent may include, for example, movie and television show dialogue,audio book text, song lyrics, and so on. The verbal media content may beobtained from closed captioning data, subtitle data, lyric data,screenplay data, audio book transcript data, and/or other data. Theverbal media content may track the audio content of the media contentfeature 142. In some cases, the verbal media content may track alternateaudio and/or verbal content of a media content feature 142 such as, forexample, an alternative language, an alternative dialogue, commentaries,etc.

The verbal time code data 148 correlates the verbal media content data145 with the media content features 142. Specifically, the verbal timecode data 148 corresponds to an index that associates words, phrases,sentences, and/or other divisions of language in the verbal mediacontent data 145 with times, frames, scenes, etc. in the correspondingmedia content feature 142. The verbal time code data 148 may includemultiple indexes for multiple languages or alternative verbal mediacontent for a media content feature 142. The search data 151 recordsdata relating to verbal media content searches performed by users. Tothis end, the search data 151 may include search queries 130, searchresults 133, search result selections 136, and/or other data. The searchdata 151 may also record the absence of search result selections 136 ofusers.

The user interest data 154 associates an expressed interest in mediacontent with users, where the expressed interest is determined relativeto search results 133 that result from verbal media content searches.The user interest data 154 is generated by the search analysisapplication 121 through analysis of the search data 151. In variousembodiments, a user may express an interest in a media content feature142 by first executing a search query 130 that produces a search result133 corresponding to the media content feature 142, and then selectingthe search result 133 to obtain further information, to view at least aportion of the media content feature 142, to add the media contentfeature 142 to a watch list, to purchase or rent the media contentfeature 142, and/or to perform other selection actions relative to thesearch result 133. The expressed user interests may be grouped by orotherwise associated with a particular language or locale, as users fromdifferent locales or using different languages may have differentreactions to verbal content.

The user profile data 157 includes various information collected from orgenerated regarding users. Such information may include purchasehistory, download history, browse history, media content viewinghistory, favorite media content, favorite categories of media content,preferences, and/or other data. The recommendations data 160 maycorrespond to recommendations 139 generated by the recommendationsapplication 127 for a user or group of users. Such recommendations 139may recommend a media content feature 142 or one or more portions of amedia content feature 142 based at least in part on user interest data154, user profile data 157, and/or other data.

The abridgement data 163 includes data describing one or moreabridgements of media content features 142 generated by the abridgementgeneration application 124. Each abridgement of a media content feature142 includes one or more portions, clips, or scenes taken from the mediacontent feature 142 based at least in part on relative popularity asdetermined through the user interest data 154. The abridgements may becustomized for a particular user or group of users, or the abridgementsmay be produced for a general audience. An abridgement, for example, maycorrespond to a customized trailer, summary, or preview for a videocontent feature.

The social network data 166 may be employed to identify relationshipsbetween users for purposes of making recommendations 139. For example,users who are related in a social network may have similar interests andmay be interested in similar media content. In some cases, notificationsmay be provided to a social network in response to a customizedabridgement, a recommendation 139, etc. being generated for a user onthe social network. The representative content portions data 169corresponds to representative portions of media content features 142that have been identified as particularly representative of therespective media content features 142 through the user interest data154. Such representative content portions may, for example, be employedfor preview or promotional purposes in a network page or other userinterface that describes the corresponding media content feature 142.

The client 106 is representative of a plurality of client devices thatmay be coupled to the network 109. The client 106 may comprise, forexample, a processor-based system such as a computer system. Such acomputer system may be embodied in the form of a desktop computer, alaptop computer, personal digital assistants, cellular telephones,smartphones, set-top boxes, music players, web pads, tablet computersystems, game consoles, electronic book readers, or other devices withlike capability. The client 106 may include a display 172. The display172 may comprise, for example, one or more devices such as liquidcrystal display (LCD) displays, gas plasma-based flat panel displays,organic light emitting diode (OLED) displays, LCD projectors, or othertypes of display devices, etc.

The client 106 may be configured to execute various applications such asa client application 175 and/or other applications. The clientapplication 175 may be executed in a client 106, for example, to accessnetwork content served up by the computing environment 103 and/or otherservers, thereby rendering a user interface 178 on the display 172. Theclient application 175 may, for example, correspond to a browser, amobile application, a media player, etc., and the user interface 178 maycorrespond to a network page, a mobile application screen, a renderedmedia content feature 142, etc. The client 106 may be configured toexecute applications beyond the client application 175 such as, forexample, browsers, mobile applications, email applications, socialnetworking applications, and/or other applications.

Next, a general description of the operation of the various componentsof the networked environment 100 is provided. To begin, users at clients106 request resources from the network content server 115. The users maybe unrecognized, recognized, or authenticated users. A form in a userinterface 178 may be rendered by the client application 175 thatfacilitates user entry of a search query 130. The search query 130 maycorrespond, for example, to a string of one or more words or partialwords. As previously discussed, the search query 130 pertains to verbalmedia content associated with media content features 142. The user maybe able to select a particular search data set according to criteria,e.g., movies, television shows, movies before 1980, a particulartelevision series, audio books by a particular author, popular moviescurrently showing in theatres, songs by a particular artist, and so on.

Upon user entry of the search query 130, the client application 175sends the search query 130 over the network 109 to the computingenvironment 103. The verbal media content search engine 118 obtains thesearch query 130 and proceeds to execute a search within the verbalmedia content data 145 based at least in part on the search query 130.The scope of the search may be limited by various criteria specified bythe user. Various search results 133 that are relevant to the searchquery 130 may be generated.

Turning now to FIG. 2, shown is one example of a user interface 178rendered by a client application 175 (FIG. 1) executed in a client 106(FIG. 1) in the networked environment 100 (FIG. 1). The user interface178 provides a non-limiting example of a listing of three search results133 a, 133 b, and 133 c. The user interface 178 provides results from asearch executed for the search query 130 of “don't look at me likethis.” A search query entry box 203 and a search execution component 206may be provided to execute additional searches.

Each of the search results 133 may provide a title, a year, and/or otherinformation about a corresponding media content feature 142 (FIG. 1).The search result 133 may identify a particular time in the mediacontent feature 142 at which the search query 130 is used, or otherwisematches verbiage that is used. A portion of the verbal media content(e.g., a quotation from the verbal media content) may be rendered inconnection with the search result 133. For video content, arepresentative image may be extracted from the video content atapproximately the time at which the matched verbiage is used.

Various user interface components may be provided in the user interface178 for the user to obtain more information about or indicate a furtherinterest in a search result 133. As non-limiting examples, the user mayclick on the representative image or another link in order to view thescene, add the scene to a watch list, add the media content feature 142to a watch list, purchase or rent the media content feature 142, viewthe media content feature 142, obtain additional information regardingthe context of the quotation, obtain additional information regardingthe media content feature 142, and/or perform other actions. Theparticular portion of the media content feature 142 that may be linkedto the search result 133 may be determined from the verbal time codedata 148 (FIG. 1). The portion may be a predefined portion (e.g., ascene, chapter, etc.) or may be dynamically determined with reference tothe matched verbal media content (e.g., beginning 10 seconds before andcontinuing 30 seconds after the matched verbal media content,coextensive with the matched verbal media content, etc.).

When the user performs any of these actions to obtain more informationabout or indicate a further interest in a search result 133, a searchresult selection 136 (FIG. 1) may be sent by the client application 175to the computing environment 103 by way of the network 109. The searchresult selection 136 may be stored and/or indexed by the verbal mediacontent search engine 118 (FIG. 1) in the search data 151 (FIG. 1) forfurther processing. It is noted that a user may perform selectionactions regarding multiple search results 133 in a listing of searchresults 133. Although the example of FIG. 2 pertains to a video contentfeature, it is understood that the example of FIG. 2 may be extended toaudio books, songs, and/or other media content that does not includevideo content.

Referring back to FIG. 1, as users execute searches and performselection actions relative to user interfaces 178 pertaining to searchresults 133, the search data 151 is populated. The search data 151 mayalso include the corresponding search queries 130. The search analysisapplication 121 performs an analysis on the search data 151 to determineuser interest in particular search results 133 as indicated by thesearch result selections 136.

Different types of search result selections 136 may be associated withdiffering levels of user interest. For example, it may be concluded thata user has a greater level of interest in a particular video contentfeature if he or she views the entire video content feature as opposedto merely viewing the selected portion that corresponds to the searchresult 133. Further, different types of media content may be associatedwith stronger or weaker levels of user interest. As a non-limitingexample, a selection of a video clip from a movie may be associated witha stronger level of user interest than a selection of an audio clip froma song.

If search results 133 are not selected for particular search queries130, it may be determined that the user does not express an interest inthe particular search results 133. In other words, the absence of searchresult selections 136 by a user may be used to infer disinterest by auser or lack of relevance of the search results 133. Also, the searchanalysis application 121 may determine that the user has expressed aninterest in a product or other subject due to the inclusion of thesubject in the search query 130 or in the verbal media contentassociated with a selected search result 133. From this analysis that isperformed, the search analysis application 121 generates the userinterest data 154.

The user interest data 154 may be used in various approaches accordingto various embodiments. In one embodiment, an abridgement generationapplication 124 processes the user interest data 154 and generatesabridgement data 163 that includes abridgements of media contentfeatures 142. The abridgement for a particular media content feature 142may exclude one or more portions of the media content feature 142. Forexample, the abridgement generation application 124 may determine themost popular scenes from a particular movie, and then dynamicallygenerate a trailer for that movie from the most popular scenes. A subsetof the most popular scenes may be selected based at least in part on apredefined maximum length for the trailer.

Customized abridgements may be generated for a particular user based atleast in part on interests expressed by the particular user or users whoare similar to the particular users. Similarity between users may bedetermined based at least in part on similar user interest data 154,similar user profile data 157, social network data 166, and so on.Customized abridgements may also be generated for a group of users orfor a general audience.

The abridgement generation application 124 or other applications may beconfigured to generate representative content portions in therepresentative content portions data 169 based at least in part on theuser interest data 154. For example, the abridgement generationapplication 124 may determine the most popular scene in a movieaccording to the number of search result selections 136. The particularscene may be designated as a representative content portion.Alternatively, an image corresponding to a time in the particular scenemay be designated as a representative content portion.

In another embodiment, the recommendations application 127 processes theuser interest data 154 to generate recommendations 139 of media contentfeatures 142 for users. The recommendations application 127 may alsoconsider user profile data 157 in addition to the user interest data154. The recommendations 139 may be presented in user interfaces 178,email messages, text messages, and/or other forms of communication. Therecommendations application 127 may recommend a particular media contentfeature 142 to a given user based at least in part on the given userperforming verbal media content searches, users related by a socialnetwork to the given user performing verbal media content searches, orusers similar to the given user performing verbal media contentsearches. The recommendations application 127 may also, or instead,generate recommendations 139 of portions of media content features 142such as, for example, scenes, chapters, etc. In some cases, a particularmedia content feature 142 or a portion thereof may be recommended basedat least in part on relative popularity as determined from the userinterest data 154.

As a non-limiting example, a user may be classified in the user profiledata 157 as being interested in comedies. The recommendationsapplication 127 may recommend media content features 142 to the userbased at least in part on search result selections 136 of other userswho are interested in comedies. As another non-limiting example, a usermay be determined to be interested in documentaries based at least inpart on the search result selections 136 of the user. Therecommendations application 127 may recommend to the user other mediacontent features 142 that are classified as documentaries.

The recommendations application 127 may also recommend a particularproduct based at least in part on the user searching for the product ora similar product in a search query 130 or the user selecting a searchresult 133 that mentions the product or a similar product. As anon-limiting example, a user may execute a search for dialogue thatincludes “Chero-Cola,” and the recommendations application 127 maygenerate recommendations 139 for the user for products relating to“Chero-Cola.” Alternatively, a user may execute a search for some otherdialogue that does not include “Chero-Cola,” and then select a searchresult 133 having dialogue that does include “Chero-Cola” or a searchresult 133 that is otherwise inferred to be relevant to “Chero-Cola.”Accordingly, the recommendations application 127 may generaterecommendations 139 for the user for products relating to “Chero-Cola.”

In various embodiments, the user interest data 154 correlated withparticular portions of a media content feature 142 may be used to renderadvertising at relatively popular portions of the media content feature142. That is to say, advertising may be preferably displayed along witha relatively popular portion of the media content feature 142. To thisend, an advertisement may be associated with the relatively popularportion based at least in part on the user interest data 154. Also, thisinformation may be used to determine pricing for advertising based atleast in part on popularity of a portion of the media content feature142 as determined through the user interest data 154. In other words, arelatively popular portion of a media content feature 142 may command arelatively higher advertising rate versus other portions.

The user interest data 154 may be used to determine relatively popularmedia content features 142 and/or relatively popular portions of mediacontent features 142. In one embodiment, a popularity rating may beassigned to the media content feature 142 or to one or more portions ofthe media content feature 142 based at least in part on quantity orfrequency of user selections, and/or on the type of user selections.Such popularity ratings may be surfaced to users in personalizedrecommendations 139, in user interfaces 178 presenting information abouta media content feature 142, in user interfaces 178 presenting searchresults 133, or elsewhere.

It is noted that a given media content feature 142 may be distributedwith verbal media content in a primary language and potentially multiplesecondary languages. Users who understand a particular language may notbe as interested in certain portions of a media content feature 142compared to users who understand a different languages. Such differencesin perception between users may have a cultural basis. For example,users in one locale who speak one language may find a particular scenehumorous, while users in another locale who speak another language mayfind the same scene distasteful. Accordingly, recommendations 139,abridgements, and so on may be generated based at least in part on userlanguages and locales.

Referring next to FIG. 3, shown is a flowchart that provides one exampleof the operation of a portion of the verbal media content search engine118 according to various embodiments. It is understood that theflowchart of FIG. 3 provides merely an example of the many differenttypes of functional arrangements that may be employed to implement theoperation of the portion of the verbal media content search engine 118as described herein. As an alternative, the flowchart of FIG. 3 may beviewed as depicting an example of steps of a method implemented in thecomputing environment 103 (FIG. 1) according to one or more embodiments.

Beginning with box 303, the verbal media content search engine 118obtains a search query 130 (FIG. 1) from a client 106 (FIG. 1) by way ofthe network 109 (FIG. 1). The search query 130 may be obtained throughdata received by the network content server 115 (FIG. 1). In box 306,the verbal media content search engine 118 executes a search of verbalmedia content data 145 (FIG. 1) based at least in part on the searchquery 130. The search may correspond to a dialogue search, a lyricssearch, an audio book text search, or other verbal media contentsearches.

In box 309, the verbal media content search engine 118 determines one ormore media content portions that are returned as a result of the search.Such portions may correspond to video clips, audio clips, etc. In oneembodiment, the verbal media content search engine 118 determines amedia content clip based at least in part on data associating a time ina media content feature 142 (FIG. 1) with the verbal media content thatmatches, substantially matches, or partially matches the search query130. In box 312, the verbal media content search engine 118 generates alisting of search results 133 (FIG. 1). In box 315, the verbal mediacontent search engine 118 sends the search results 133 to the client 106through the network content server 115. In box 318, the verbal mediacontent search engine 118 obtains a search result selection 136 from theclient 106. The search result selection 136 may correspond to a userselecting a particular search result 133 to view a detail page, add acorresponding media content feature 142 to a watch list, to view orplayback all or a portion of a corresponding media content feature 142,and or other types of selections. In box 321, the verbal media contentsearch engine 118 records the search result selection 136 andpotentially the search query 130 in the search data 151 (FIG. 1).

In box 324, the verbal media content search engine 118 determineswhether another search result selection 136 from the search results 133is received. If another search result selection 136 is received, theverbal media content search engine 118 returns to box 318 and obtainsthe next search result selection 136. Otherwise, the verbal mediacontent search engine 118 continues to box 327.

In box 327, the verbal media content search engine 118 employs thesearch analysis application 121 (FIG. 1) to analyze the search data 151.In doing so, the search analysis application 121 determines whether theuser has expressed an interest in one or more of the portions of mediacontent features 142 corresponding to search results 133. Accordingly,the search analysis application 121 generates user interest data 154(FIG. 1) in response to users expressing an interest in one or more ofthe search results 133. In some cases, the user interest data 154describes an aggregate interest of a group of users in particular mediacontent.

In box 330, the verbal media content search engine 118 employs therecommendations application 127 (FIG. 1), the abridgement generationapplication 124 (FIG. 1), and/or other applications to generaterecommendations 139 (FIG. 1), abridgements, representative contentportions, and/or other data based at least in part on the user interestdata 154. In some cases, a content classification (e.g., a genre, a plotelement type, etc.) may be assigned to a portion of a media contentfeature 142 based at least in part on an interest expressed by a userand user profile data 157 (FIG. 1) associated with the user. Forexample, a user may be profiled as being a fan of comedy, and in somecases, a video clip may be classified as comedy based at least in parton the demonstrated interest of the user and the profile of the user.

In generating recommendations 139, the recommendations application 127may recommend other media content features 142 to the user, recommendthe selected one of the portions of the media content feature 142 toother users, and so on based at least in part on the user interest data154. The recommendations application 127 may determine a relationshipbetween a user and other users based at least in part on an interest ofthe user in media content as expressed in the user interest data 154.The other user may be determined based at least in part on anassociation between a secondary language and the other user. The otheruser may also be determined based at least in part on social networkdata 166 (FIG. 1) indicating a relationship between the user and theother user.

In generating an abridgement, the abridgement generation application 124may generate the abridgement based at least in part on a maximum length,and the abridgement may include a first portion of the media contentfeature 142 and a second portion of the media content feature 142 whileexcluding a third portion of the media content feature 142 that isbetween the first and second portions. Thereafter, the portion of theverbal media content search engine 118 ends.

With reference to FIG. 4, shown is a schematic block diagram of thecomputing environment 103 according to an embodiment of the presentdisclosure. The computing environment 103 includes one or more computingdevices 400. Each computing device 400 includes at least one processorcircuit, for example, having a processor 403 and a memory 406, both ofwhich are coupled to a local interface 409. To this end, each computingdevice 400 may comprise, for example, at least one server computer orlike device. The local interface 409 may comprise, for example, a databus with an accompanying address/control bus or other bus structure ascan be appreciated.

Stored in the memory 406 are both data and several components that areexecutable by the processor 403. In particular, stored in the memory 406and executable by the processor 403 are the network content server 115,the verbal media content search engine 118, the search analysisapplication 121, the abridgement generation application 124, therecommendations application 127, and potentially other applications.Also stored in the memory 406 may be a data store 112 and other data. Inaddition, an operating system may be stored in the memory 406 andexecutable by the processor 403.

It is understood that there may be other applications that are stored inthe memory 406 and are executable by the processor 403 as can beappreciated. Where any component discussed herein is implemented in theform of software, any one of a number of programming languages may beemployed such as, for example, C, C++, C#, Objective C, Java®,JavaScript®, Perl, PHP, Visual Basic®, Python®, Ruby, Flash®, or otherprogramming languages.

A number of software components are stored in the memory 406 and areexecutable by the processor 403. In this respect, the term “executable”means a program file that is in a form that can ultimately be run by theprocessor 403. Examples of executable programs may be, for example, acompiled program that can be translated into machine code in a formatthat can be loaded into a random access portion of the memory 406 andrun by the processor 403, source code that may be expressed in properformat such as object code that is capable of being loaded into a randomaccess portion of the memory 406 and executed by the processor 403, orsource code that may be interpreted by another executable program togenerate instructions in a random access portion of the memory 406 to beexecuted by the processor 403, etc. An executable program may be storedin any portion or component of the memory 406 including, for example,random access memory (RAM), read-only memory (ROM), hard drive,solid-state drive, USB flash drive, memory card, optical disc such ascompact disc (CD) or digital versatile disc (DVD), floppy disk, magnetictape, or other memory components.

The memory 406 is defined herein as including both volatile andnonvolatile memory and data storage components. Volatile components arethose that do not retain data values upon loss of power. Nonvolatilecomponents are those that retain data upon a loss of power. Thus, thememory 406 may comprise, for example, random access memory (RAM),read-only memory (ROM), hard disk drives, solid-state drives, USB flashdrives, memory cards accessed via a memory card reader, floppy disksaccessed via an associated floppy disk drive, optical discs accessed viaan optical disc drive, magnetic tapes accessed via an appropriate tapedrive, and/or other memory components, or a combination of any two ormore of these memory components. In addition, the RAM may comprise, forexample, static random access memory (SRAM), dynamic random accessmemory (DRAM), or magnetic random access memory (MRAM) and other suchdevices. The ROM may comprise, for example, a programmable read-onlymemory (PROM), an erasable programmable read-only memory (EPROM), anelectrically erasable programmable read-only memory (EEPROM), or otherlike memory device.

Also, the processor 403 may represent multiple processors 403 and/ormultiple processor cores and the memory 406 may represent multiplememories 406 that operate in parallel processing circuits, respectively.In such a case, the local interface 409 may be an appropriate networkthat facilitates communication between any two of the multipleprocessors 403, between any processor 403 and any of the memories 406,or between any two of the memories 406, etc. The local interface 409 maycomprise additional systems designed to coordinate this communication,including, for example, performing load balancing. The processor 403 maybe of electrical or of some other available construction.

Although the network content server 115, the verbal media content searchengine 118, the search analysis application 121, the abridgementgeneration application 124, the recommendations application 127, andother various systems described herein may be embodied in software orcode executed by general purpose hardware as discussed above, as analternative the same may also be embodied in dedicated hardware or acombination of software/general purpose hardware and dedicated hardware.If embodied in dedicated hardware, each can be implemented as a circuitor state machine that employs any one of or a combination of a number oftechnologies. These technologies may include, but are not limited to,discrete logic circuits having logic gates for implementing variouslogic functions upon an application of one or more data signals,application specific integrated circuits (ASICs) having appropriatelogic gates, field-programmable gate arrays (FPGAs), or othercomponents, etc. Such technologies are generally well known by thoseskilled in the art and, consequently, are not described in detailherein.

The flowchart of FIG. 3 shows the functionality and operation of animplementation of portions of the verbal media content search engine 118and potentially other applications. If embodied in software, each blockmay represent a module, segment, or portion of code that comprisesprogram instructions to implement the specified logical function(s). Theprogram instructions may be embodied in the form of source code thatcomprises human-readable statements written in a programming language ormachine code that comprises numerical instructions recognizable by asuitable execution system such as a processor 403 in a computer systemor other system. The machine code may be converted from the source code,etc. If embodied in hardware, each block may represent a circuit or anumber of interconnected circuits to implement the specified logicalfunction(s).

Although the flowchart of FIG. 3 shows a specific order of execution, itis understood that the order of execution may differ from that which isdepicted. For example, the order of execution of two or more blocks maybe scrambled relative to the order shown. Also, two or more blocks shownin succession in FIG. 3 may be executed concurrently or with partialconcurrence. Further, in some embodiments, one or more of the blocksshown in FIG. 3 may be skipped or omitted. In addition, any number ofcounters, state variables, warning semaphores, or messages might beadded to the logical flow described herein, for purposes of enhancedutility, accounting, performance measurement, or providingtroubleshooting aids, etc. It is understood that all such variations arewithin the scope of the present disclosure.

Also, any logic or application described herein, including the networkcontent server 115, the verbal media content search engine 118, thesearch analysis application 121, the abridgement generation application124, and the recommendations application 127, that comprises software orcode can be embodied in any non-transitory computer-readable medium foruse by or in connection with an instruction execution system such as,for example, a processor 403 in a computer system or other system. Inthis sense, the logic may comprise, for example, statements includinginstructions and declarations that can be fetched from thecomputer-readable medium and executed by the instruction executionsystem. In the context of the present disclosure, a “computer-readablemedium” can be any medium that can contain, store, or maintain the logicor application described herein for use by or in connection with theinstruction execution system.

The computer-readable medium can comprise any one of many physical mediasuch as, for example, magnetic, optical, or semiconductor media. Morespecific examples of a suitable computer-readable medium would include,but are not limited to, magnetic tapes, magnetic floppy diskettes,magnetic hard drives, memory cards, solid-state drives, USB flashdrives, or optical discs. Also, the computer-readable medium may be arandom access memory (RAM) including, for example, static random accessmemory (SRAM) and dynamic random access memory (DRAM), or magneticrandom access memory (MRAM). In addition, the computer-readable mediummay be a read-only memory (ROM), a programmable read-only memory (PROM),an erasable programmable read-only memory (EPROM), an electricallyerasable programmable read-only memory (EEPROM), or other type of memorydevice.

It should be emphasized that the above-described embodiments of thepresent disclosure are merely possible examples of implementations setforth for a clear understanding of the principles of the disclosure.Many variations and modifications may be made to the above-describedembodiment(s) without departing substantially from the spirit andprinciples of the disclosure. All such modifications and variations areintended to be included herein within the scope of this disclosure andprotected by the following claims.

Therefore, the following is claimed:
 1. A non-transitorycomputer-readable medium embodying a program executable in at least onecomputing device, comprising: code that obtains a plurality of dialoguesearch queries from a plurality of users; code that, for individual onesof the plurality of dialogue search queries, determines a respectiveplurality of clips from a plurality of video content features byexecuting a respective dialogue search based at least in part on theindividual ones of the plurality of dialogue search queries; code thatsends a corresponding dialogue search result listing of the respectiveplurality of clips to respective ones of the plurality of users; codethat determines that the plurality of users has expressed an interest inat least two of the clips of one of the plurality of video contentfeatures via the corresponding dialogue search result listing; and codethat generates an abridgement of the one of the plurality of videocontent features based at least in part on the interest in the at leasttwo of the clips, the abridgement including a first portion of the oneof the plurality of video content features and a second portion of theone of the plurality of video content features and excluding a thirdportion of the one of the plurality of video content features that isbetween the first portion and the second portion.
 2. The non-transitorycomputer-readable medium of claim 1, further comprising code thatrecommends another video content feature to one of the plurality ofusers based at least in part on the interest in the at least two of theclips.
 3. The non-transitory computer-readable medium of claim 1,further comprising code that recommends the at least two of the clips toanother user.
 4. The non-transitory computer-readable medium of claim 1,wherein the abridgement is generated based at least in part on a maximumlength.
 5. The non-transitory computer-readable medium of claim 1,further comprising code that stores user interest data in response tothe plurality of users expressing the interest.
 6. The non-transitorycomputer-readable medium of claim 1, further comprising code thatrecommends another video content feature to at least one of theplurality of users based at least in part on the interest.
 7. Thenon-transitory computer-readable medium of claim 1, wherein therespective dialogue search is executed relative to dialogue in asecondary language, and further comprising code that recommends theabridgement to another user based at least in part on an associationbetween the secondary language and the other user.
 8. A system,comprising: at least one computing device; and at least one applicationexecutable by the at least one computing device, the at least oneapplication comprising: logic that obtains a plurality of search queriesfrom a plurality of users; logic that determines a plurality of mediacontent items by executing a plurality of verbal media content searchesbased at least in part on the plurality of search queries, the pluralityof media content items including verbal media content that is relevantto respective ones of the plurality of search queries; and logic thatgenerates a customized abridgement of a media content feature based atleast in part on an interest expressed by the plurality of users in aplurality of portions of the media content via the plurality of verbalmedia content searches, the customized abridgement including a firstportion of the media content feature and a second portion of the mediacontent feature and excluding a third portion of the media contentfeature that is between the first portion and the second portion.
 9. Thesystem of claim 8, wherein the customized abridgement is generated basedat least in part on a maximum length.
 10. The system of claim 8, whereinthe at least one application further comprises: logic that determines aproduct relevant to the plurality of media content items; and logic thatassociates an interest in the product with at least one of the pluralityof users in response to the interest expressed by the plurality ofusers.
 11. The system of claim 8, wherein the plurality of media contentitems includes a media content clip, and the at least one applicationfurther comprises logic that determines the media content clip based atleast in part on data associating a time in a media content feature withthe verbal media content that is relevant to at least one of theplurality of search queries.
 12. The system of claim 11, wherein themedia content feature corresponds to a video content feature, and thedata associating the time in the media content feature with the verbalmedia content that is relevant to the at least one of the plurality ofsearch queries corresponds to verbal time code data for the videocontent feature.
 13. The system of claim 11, wherein the media contentfeature corresponds to an audio content feature, and the dataassociating the time in the media content feature with the verbal mediacontent that is relevant to the at least one of the plurality of searchqueries query corresponds to verbal time code data for the audio contentfeature.
 14. The system of claim 8, wherein the interest is determinedbased at least in part on data indicating a user selection of one of theplurality of media content items from a search result listing.
 15. Thesystem of claim 8, wherein the interest is determined based at least inpart on data indicating an absence of a user selection of one of theplurality of media content items from a search result listing.
 16. Thesystem of claim 14, wherein the user selection corresponds to a playbackof the one of the at least one media content item.
 17. The system ofclaim 14, wherein the user selection corresponds to a request foradditional information regarding the one of the plurality of mediacontent items.
 18. The system of claim 8, wherein the at least oneapplication further comprises logic that determines a relationshipbetween at least one of the plurality of users and another user based atleast in part on the interest.
 19. The system of claim 8, wherein the atleast one application further comprises logic that generates arecommendation of other media content for at least one of the pluralityof users based at least in part on the interest.
 20. The system of claim8, wherein the at least one application further comprises logic thatassociates an advertisement with one of the plurality of media contentitems based at least in part on the interest.
 21. The system of claim 8,wherein the at least one application further comprises logic thatassigns a popularity rating to the media content based at least in parton the interest.
 22. A method, comprising: obtaining, by at least onecomputing device, a search query from a first user; determining, by theat least one computing device, a portion of a media content feature byexecuting a verbal media content search based at least in part on thesearch query, the portion of the media content feature including verbalmedia content that matches the search query, the verbal media contentcorresponding to a secondary language of the media content feature;determining, by the at least one computing device, that the first userexpresses an interest in the portion of the media content featurerelative to a search result listing; determining, by the at least onecomputing device, a second user based at least in part on an associationbetween the secondary language and the second user; and recommending, bythe at least one computing device, the media content to the second userbased at least in part on the interest expressed by the first user inthe portion of the media content feature.
 23. The method of claim 22,further comprising: generating, by the at least one computing device, acustomized abridgement of another media content feature, the customizedabridgement including a plurality of portions of the other media contentfeature, the portions being selected based at least in part on theinterest expressed by the first user in the portion of the media contentfeature.
 24. The method of claim 22, further comprising generating, bythe at least one computing device, an abridgement of the media contentfeature that includes the portion of the media content feature, theportion being included in the abridgement based at least in part on theinterest expressed by the first user in the portion of the media contentfeature.
 25. The method of claim 22, further comprising determining, bythe at least one computing device, the second user based at least inpart on social network data indicating a relationship between the firstuser and the second user.
 26. The method of claim 22, further comprisingassigning, by the at least one computing device, a contentclassification to the portion of the media content feature based atleast in part on the interest expressed by the user and profile dataassociated with the user.
 27. The method of claim 22, further comprisingselecting, by the at least one computing device, the portion of themedia content feature as a representative portion of the media contentfeature based at least in part on the interest expressed by the user.28. The method of claim 23, further comprising recommending, by the atleast one computing device, the customized abridgement of the othermedia content feature to the first user.
 29. The method of claim 22,further comprising recommending, by the at least one computing device,media content to the first user based at least in part on the interestexpressed by the first user in the portion of the media content feature.